~$ ls -la posts/ | grep llm
Jun 19 2026 #AI |#LLM |#computer-vision |#MediaPipe |#llama.cpp |#Python |#biomechanics

Analyzing Deadlift Form with Computer Vision and a Local LLM

I took a deadlift video from r/formcheck, fed it to a Python script, and got back prioritized coaching cues. All running locally on my laptop. No cloud APIs, no subscriptions. Just MediaPipe for pose estimation, OpenCV for frame extraction, and Qwen 3.5-9B running on llama.cpp for natural-language feedback. The Idea r/formcheck is a subreddit where lifters post videos of their sets and ask for feedback. You film your deadlift, upload it, and wait for someone (hopefully qualified) to tell you what to fix. The problem is that feedback is inconsistent, slow, and often contradictory. [read more]
May 14 2026 #AI |#LLM |#llama.cpp |#Qwen |#Pi |#local-inference

How to run Qwen3.5-9B with llama.cpp and Pi

I ran Qwen3.5-9B, a 4-bit quantized model, locally on my MacBook Pro M4 Pro with 24 GB of RAM, pointed a terminal coding agent at it, and asked it to build a checkout page with the Stripe API. It did. No cloud, no API calls to OpenAI, no token costs. Just a model running on my laptop. Here’s how. Qwen 3.5 Qwen3.5 is Alibaba’s latest open-weight language model family. The 9B variant sits in a sweet spot: large enough to be genuinely useful for coding tasks, small enough to run on consumer hardware once quantized. It supports a 256K token context window and performs competitively with much larger models on coding benchmarks. [read more]
Apr 24 2026 #AI |#LLM |#Ollama |#Next.js |#PostgreSQL |#Vector Search |#Python

Building an AI-Powered Salary Search Engine with Local LLMs and Vector Search

What if you could search for salaries not by exact keywords, but by describing a job in natural language? “Senior developer in Brussels with a company car” or “nurse working in Antwerp” — and get relevant results based on semantic similarity rather than string matching. This is exactly what I built with BeSalary: an AI-powered salary search engine that extracts structured data from Reddit posts using local LLMs and enables semantic search through vector embeddings. You can try it live at besalary-wine.vercel.app. [read more]